reflect

A note-connection skill that finds meaningful relationships between notes and updates MOCs, or maps of content that organize related notes.

In plain words
What is it for?
Use it after creating notes to discover related ideas, add links, update topic maps, and synthesize material around a topic.
Why use it?
It helps turn separate notes into a connected knowledge graph and keeps topic maps up to date.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agenticnotetaking/arscontexta/reflect
Any agent
npx skills add agenticnotetaking/arscontexta --skill reflect
Clone the repo
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta

Made for: Claude Code, Codex.

Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,589 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00062 $0.06589
Opus 5 $0.00031 $0.03295
Sonnet 5 $0.00012 $0.01318
Haiku 4.5 $0.00006 $0.00659

Measured 2d ago against content hash 98fef2d260f4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

reflect scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf "$LOCKDIR"
skill-sources/reflect/SKILL.md · 747 lines

How it starts

The opening of the file, as written. The whole thing — 747 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

  1. ops/derivation-manifest.md — vocabulary mapping, platform hints

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.note / vocabulary.note_plural for note type references
    • Use vocabulary.reflect for the process verb in output
    • Use vocabulary.topic_map / vocabulary.topic_map_plural for MOC references
    • Use vocabulary.cmd_reweave for the next-phase suggestion
    • Use vocabulary.inbox for the inbox folder name
  2. ops/config.yaml — processing depth, pipeline chaining

    • processing.depth: deep | standard | quick
    • processing.chaining: manual | suggested | automatic

If these files don't exist, use universal defaults.

Processing depth adaptation:

Depth Connection Behavior
deep Full dual discovery (MOC + semantic search). Evaluate every candidate. Multiple passes. Synthesis opportunity detection. Bidirectional link evaluation for all connections.
standard Dual discovery with top 5-10 candidates. Standard evaluation. Bidirectional check for strong connections only.
quick Single pass — either MOC or semantic search. Accept obvious connections only. Skip synthesis detection.

EXECUTE NOW

Target: $ARGUMENTS

Parse immediately:

  • If target contains [[note name]] or note name: find connections for that {vocabulary.note}
  • If target contains --handoff: output RALPH HANDOFF block at end
  • If target is empty: check for recently created {vocabulary.note_plural} or ask which {vocabulary.note}
  • If target is "recent" or "new": find connections for all {vocabulary.note_plural} created today

Execute these steps:

  1. Read the target {vocabulary.note} fully — understand its claim and context
  2. Throughout discovery: Capture which {vocabulary.topic_map_plural} you read, which queries you ran (with scores), which candidates you evaluated. This becomes the Discovery Trace — proving methodology was followed, not reconstructed.
  3. Run Phase 0 (index freshness check)
  4. Use dual discovery in parallel:
    • Browse relevant {vocabulary.topic_map}(s) for related {vocabulary.note_plural}
    • Run semantic search for conceptually related {vocabulary.note_plural}
  5. Evaluate each candidate: does a genuine connection exist? Can you articulate WHY?
  6. Add inline wiki-links where connections pass the articulation test
  7. Update relevant {vocabulary.topic_map}(s) with this {vocabulary.note}
  8. If task file in context: update the {vocabulary.reflect} section
  9. Report what was connected and why
  10. If --handoff in target: output RALPH HANDOFF block

Read the full file on GitHub · 747 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 747 lines · 62 tokens per session scan C 98fef2d260f4

Subscribe to this mod's changes

reflect is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 62 tokens to every session and 6,589 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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